Preparation and Characterization of Polyvinylpyrrolidone-functionalized Multiwalled Carbon Nanotube (PVP/f-MWNT) Nanocomposites

Polyvinylpyrrolidone/hydroxyl-functionalized multiwalled carbon nanotube and sulfonyl-functionalized multiwalled carbon nanotube nanocomposites were prepared in aqueous media. The structure, morphology, and thermal characterization of the prepared nanocomposites were done by Fourier transform infrared, scanning electron microscopy, thermogravimetric analysis, and differential scanning calorimetry techniques. The polyvinylpyrrolidone/hydroxyl-functionalized multiwalled carbon nanotube and polyvinylpyrrolidone/sulfonyl-functionalized multiwalled carbon nanotube nanocomposites improved the thermal properties of polyvinylpyrrolidone. According to the differential scanning calorimetry analysis, the glass transition temperature of 101.6 and 84.6°C is observed for the polyvinylpyrrolidone/hydroxyl-functionalized multiwalled carbon nanotube (5% w/w) and polyvinylpyrrolidone/sulfonyl-functionalized multiwalled carbon nanotube (5% w/w) nanocomposites, respectively. The energy-dispersive X-ray spectroscopy image of polyvinylpyrrolidone/sulfonyl-functionalized multiwalled carbon nanotube (5% w/w) nanocomposite showed a homogenous distribution of sulfonyl-functionalized multiwalled carbon nanotube in the polyvinylpyrrolidone matrix.     

Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach

We propose a bi-objective cell formation problem with demand of products expressed in a number of probabilistic scenarios. To deal with the uncertain demand of products, a framework of two-stage stochastic programming model is presented. The proposed model considers minimizing the sum of the miscellaneous costs (machine constant cost, expected machine variable cost, cell fixed-charge cost, and expected intercell movement cost) and expected total cell loading variation. Because of conflicting objectives, we develop a two-phase fuzzy linear programming approach for solving bi-objective cell formation problem. To show the effectiveness of the proposed approach, numerical examples are solved and the results are compared with the two existing approaches in the literature. The computational results show that the proposed fuzzy method achieves lower objective functions as well as higher satisfaction degrees.     

A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture

The Journal of Supercomputing - Recently, with the expansion of communications and generated data, the need for processing this high volume of data in minimum time and maximum speed has increased....     

Cyclic scheduling of a robotic flexible cell with load lock and swap

In this paper, we study the problem of robotic cell scheduling with m machines with flexibility, load lock and swap assumptions. The robotic cell repetitively produces parts of identical types. We determine the cycle time of all 1-unit cycles in this type of robotic cell and present two new lower bounds for robot move cycles with load lock and swap, either there is flexibility or inflexibility. We also provide a new robot move cycle and prove that it dominates all classical robot move cycles considered in the existing literature of m-machine robotic cells.     

In situ solvent formation microextraction based on a new ionic liquid for green preconcentration of trace amount of Cu (II) ions in water samples

A simple, fast, and effective analytical technique known as in situ solvent formation microextraction was used to preconcentrate/separate trace amounts of Cu(II) ions in water samples prior to determination by flame atomic absorption spectroscopy. In the present method, 6,6′-(1Z,1′Z)(butane-1,4-diylbis(azan-1-yl-1-ylidene)bis(methan-1-yl-1-ylidene)bis-3-bromophenol (Schiff base ligand) as the complexing agent and 1-methyl-3-pentylimidazolium bromide (ionic liquid) as an extracting agent were successfully synthesized and characterized by FTIR, C-NMR, and H-NMR spectroscopies. The effects of several analytical parameters on the method were studied and optimized, and the merits of the method, such as LDR (0.2–1000 µg L^?1), LOD (0.12 µg L^?1), RSD (4.1%), and preconcentration factor (70) were evaluated.     

Effect of mixer geometry and operating conditions on flow mixing of shear thinning fluids with yield stress

Flow mixing of a non‐Newtonian fluid in a stirred tank equipped with a side‐entry impeller was observed using particle image velocimetry (PIV). The effects of some geometrical parameters including the mixer shape and impeller type and position on the flow pattern were studied on velocity fields obtained at different locations inside the mixing domain. The different flow structures revealed that the ratio of inertial and viscous forces largely defines the flow pattern. Dead zones were observed inside the tank due to the rheological properties of the fluid. The size of the dynamic regions and the average velocity near the impeller were enhanced by increasing the suction area. Likewise, large pitch ratios were found to improve the active mixing zone and the axial discharge. Curves for the power and pumping numbers are reported for different axial flow impellers. © 2013 American Institute of Chemical Engineers AIChE J, 60: 1156–1167, 2014     

Investigation of well test behavior in gas condensate reservoir using single-phase pseudo-pressure function

Gas condensate reservoirs present complicated thermodynamic behavior when pressure falls below the dew point pressure, due to fluid dropout and change in the fluid composition. Condensate blockage in the near wellbore region reduces the well deliverability. Mixture composition change in the reservoir makes the interpretation of well tests in gas condensate reservoirs a serious challenge. In this study, at first the capillary number effect and Non-Darcy Flow on compositional simulation of gas condensate reservoirs were investigated and then well test analysis was carried out. The main objective of this work was to examine gas condensate well test analysis using single-phase gas pseudo-pressure and radial composite model assuming capillary number effect and Non-Darcy Flow. For this purpose some fluid samples were selected and results compared. Results indicate that estimation of reservoir properties below the dew point is in good agreement with actual input, particularly for lean fluid samples.     

Resistance of concrete protected by fabric to projectile impact

This paper reports on impact behavior of concrete panels protected by Polypropylene and Zylon fabric, respectively. Concrete panels were cast with different thickness and subjected to impact by a steel projectile. The initial and residual velocities were measured experimentally and the energy absorbed by the different concrete panels with and without fabric was calculated. All concrete panels were able to absorb almost all the kinetic energy of the projectiles. For concrete panels protected by fabric scabbing of concrete from the back face was considerably reduced and the debris contained by the fabric. Upper and lower bounds are proposed for energy absorbed per unit thickness and penetration results are compared with the available empirical formulas. It is shown that current penetration equations do not accurately predict impact parameters for concrete protected by fabric.     

Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing–heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict production-related challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing–heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing–heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.